Use for free/open reverse engineering with Ghidra (headless or GUI), including decompile, cross-refs, and optional Ghidra MCP workflows when IDA is unavailable.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
binary-diff / patch-diff-exploit 的 ghidriff 联动| 需求 | 优先 |
|---|---|
| 已有 IDA MCP 深挖 | ida-reverse/ |
| 开源 / 批量 / 教学 | 本 skill |
| 仅 CLI 快速侦察 | radare2/ |
□ 新建 Project → Import 文件 → Analyze（默认分析器）
□ 记录语言/编译器识别结果与基址
□ 标记入口、导出表、字符串 xref
□ 从字符串 / 导入 API 反查
□ Decompile 窗口还原算法
□ 重命名函数/变量；写 Plate comment
□ 需要动态时交接 Frida/GDB（reverse-engineering 动态章）
# 示例：analyzeHeadless 路径因安装而异，MUST 确认本机实际安装路径
analyzeHeadless /path/to/project Proj -import sample.bin -postScript ExportDecomp.py
□ 确认 ghidra MCP 端口（常见 8765，以实际环境为准）
□ 用 MCP 工具拉反编译 / xrefs，禁止猜端口
| 工具 | 用途 |
|---|---|
| Ghidra | 反编译主工具 |
| ghidra-mcp | AI 桥 |
| ghidriff | 补丁差分，见 patch-diff-exploit |
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Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.
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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.
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Create AI marketing videos for ads, promos, product launches, and brand content. Models: Veo, Seedance, Wan, FLUX for visuals, Kokoro for voiceover. Types: product demos, testimonials, explainers, social ads, brand videos. Use for: Facebook ads, YouTube ads, product launches, brand awareness. Triggers: marketing video, ad video, promo video, commercial, brand video, product video, explainer video, ad creative, video ad, facebook ad video, youtube ad, instagram ad, tiktok ad, promotional video, launch video
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Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.
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Your AI research and engineering brain trust. 59 named personas across 8 cells covering frontier labs, applied product, model architecture, reasoning/RL/agents, alignment and interpretability, theory and science of DL, multimodal and…
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Use when designing a new REST or GraphQL API, reviewing an API spec before implementation, setting team API standards, or migrating REST to GraphQL. Covers resources, HTTP semantics, pagination, error handling, and pitfalls.
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